SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

Showing 38513875 of 10420 papers

TitleStatusHype
Image-Caption Encoding for Improving Zero-Shot GeneralizationCode0
IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural NetworksCode0
Exploring Randomly Wired Neural Networks for Image RecognitionCode0
ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain AdaptationCode0
DATA: Differentiable ArchiTecture ApproximationCode0
Adapting Object Detectors via Selective Cross-Domain AlignmentCode0
Image classification in frequency domain with 2SReLU: a second harmonics superposition activation functionCode0
Data-dependent Initializations of Convolutional Neural NetworksCode0
Identifying Adversarially Attackable and Robust SamplesCode0
Architectural Vision for Quantum Computing in the Edge-Cloud ContinuumCode0
Exploring the Benefits of Visual Prompting in Differential PrivacyCode0
ColorMAE: Exploring data-independent masking strategies in Masked AutoEncodersCode0
Identifying Bias in Deep Neural Networks Using Image TransformsCode0
Model Input-Output Configuration Search with Embedded Feature Selection for Sensor Time-series and Image ClassificationCode0
IDEA: Image Description Enhanced CLIP-AdapterCode0
Identification of Stone Deterioration Patterns with Large Multimodal ModelsCode0
Identifying Transients in the Dark Energy Survey using Convolutional Neural NetworksCode0
Exploring the Limits of Weakly Supervised PretrainingCode0
Exploring the Open World Using Incremental Extreme Value MachinesCode0
Adversarial Style Augmentation for Domain Generalized Urban-Scene SegmentationCode0
I-CEE: Tailoring Explanations of Image Classification Models to User ExpertiseCode0
Cartoon Face Recognition: A Benchmark DatasetCode0
iCLIP: Bridging Image Classification and Contrastive Language-Image Pre-Training for Visual RecognitionCode0
iCAR: Bridging Image Classification and Image-text Alignment for Visual RecognitionCode0
Accelerating Targeted Hard-Label Adversarial Attacks in Low-Query Black-Box SettingsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94Unverified
4DaViT-GTop 1 Accuracy90.4Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2Unverified
6DaViT-HTop 1 Accuracy90.2Unverified
7SwinV2-GTop 1 Accuracy90.17Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05Unverified
10RevCol-HTop 1 Accuracy90Unverified